DifferenceInDifferences

class causalpy.experiments.diff_in_diff.DifferenceInDifferences

A class to analyse data from Difference in Difference settings.

Note

There is no pre/post intervention data distinction for DiD, we fit all the data available.

Parameters:

• data (DataFrame) – A pandas dataframe

• formula (str) – A statistical model formula

• time_variable_name (str) – Name of the data column for the time variable

• group_variable_name (str) – Name of the data column for the group variable

• post_treatment_variable_name (str) – Name of the data column indicating post-treatment period (default: “post_treatment”)

• model – A PyMC model for difference in differences

Example

>>> import causalpy as cp
>>> df = cp.load_data("did")
>>> seed = 42
>>> result = cp.DifferenceInDifferences(
...     df,
...     formula="y ~ 1 + group*post_treatment",
...     time_variable_name="t",
...     group_variable_name="group",
...     model=cp.pymc_models.LinearRegression(
...         sample_kwargs={
...             "target_accept": 0.95,
...             "random_seed": seed,
...             "progressbar": False,
...         }
...     ),
... )

Methods

DifferenceInDifferences.__init__(data, ...)
DifferenceInDifferences.get_plot_data(*args, ...)	Recover the data of an experiment along with the prediction and causal impact information.
DifferenceInDifferences.get_plot_data_bayesian(...)	Abstract method for recovering plot data.
DifferenceInDifferences.get_plot_data_ols(...)	Abstract method for recovering plot data.
DifferenceInDifferences.input_validation()
DifferenceInDifferences.plot(*args, **kwargs)	Plot the model.
DifferenceInDifferences.print_coefficients([...])	Ask the model to print its coefficients.
DifferenceInDifferences.summary([round_to])	Print summary of main results and model coefficients.

Attributes

idata	Return the InferenceData object of the model.
supports_bayes
supports_ols

__init__(data, formula, time_variable_name, group_variable_name, post_treatment_variable_name='post_treatment', model=None, **kwargs)

Parameters:

• data (DataFrame)

• formula (str)

• time_variable_name (str)

• group_variable_name (str)

• post_treatment_variable_name (str)

Return type:

None

classmethod __new__(*args, **kwargs)